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Activity Number: 459 - Statistical Methods for Social Interactions
Type: Invited
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract #320404
Title: Measuring and Modeling Neighborhoods
Author(s): Cory McCartan* and Kosuke Imai and Jacob Brown
Companies: Harvard University and Harvard University and Harvard University
Keywords: hierarchical model; Bayesian; residential segregation; surveys; measurement
Abstract:

With the availability of granular geographical data, social scientists are increasingly interested in examining how residential neighborhoods are formed and how they influence attitudes and behavior. To facilitate such studies, we develop an easy-to-use online survey instrument that allows respondents to draw their neighborhoods on a map. We then propose a statistical model that can be used to analyze how the characteristics of respondents, those of relevant local areas, and their interactions shape their subjective neighborhoods. The model also generates out-of-sample predictions of one’s neighborhood given these observed characteristics. We illustrate the proposed methodology by conducting a survey among registered voters in Miami, New York City, and Phoenix. We find that across these cities voters are more likely to include same-race and co-partisan census blocks in their neighborhoods. We also show that our model provides more accurate out-of-sample predictions than the standard distance-based measures of neighborhoods.


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